发表机构
INFOTEC Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación（墨西哥信息通信技术研究与创新中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种零样本、无需训练的盲彩色图像篡改定位流程，通过噪声残留伪影模式分析实现，无需训练数据或设备注册，在与现有最先进被动方法的对比中展现出竞争力。
AI 中文摘要
数码相机会通过去马赛克、相机内后处理和有损压缩，在每张采集的图像中嵌入设备特定伪影，这些痕迹构成可用于评估图像真实性的取证信号。现有被动方法主要依赖拜耳残差的绿色通道，丢弃了其余颜色通道的相关信息，且通常需要训练数据或设备注册。本研究提出一种零样本、无需训练的盲图像篡改定位流程，该流程可直接从单张可疑图像的噪声残差中估计参考伪影模式，无需假设固定的滤波器配置、颜色布局或块周期。该流程包含基于采集-插值噪声方差比的原则性去噪器选择准则、针对估计参考模式的块级相关性分析，以及生成像素级篡改概率图的双分量高斯混合模型评分阶段。消融研究评估了去噪器选择和块大小对定位精度的影响，与现有最先进被动方法的对比表明，所提零样本方法具有竞争力。
英文摘要
Digital cameras embed device-specific artifacts into every acquired image through demosaicing, in-camera post-processing, and lossy compression. These traces constitute a forensic signal that can be exploited to assess image authenticity. Existing passive methods rely predominantly on the green channel of the Bayer residual, discarding the correlated information available in the remaining color channels and typically requiring training data or device enrollment. This work proposes a zero-shot, training-free blind image manipulation localization pipeline that estimates a reference artifact pattern directly from the noise residual of a single suspect image, without assuming a fixed filter configuration, color layout, or block period. The pipeline incorporates a principled denoiser selection criterion based on the acquired-to-interpolated noise variance ratio, a block-level correlation analysis against the estimated reference pattern, and a two-component Gaussian Mixture Model scoring stage that produces a pixel-level tampering probability map. An ablation study evaluates the impact of denoiser choice and block size on localization accuracy, and comparisons against state-of-the-art passive methods demonstrate the competitiveness of the proposed zero-shot approach.
